I am using scikit-learn for classification of text documents(22000) to 100 classes. I use scikit-learn's confusion matrix method for computing the confusion matrix.
model1 = LogisticRegression()
model1 = model1.fit(matrix, labels)
pred = model1.predict(test_matrix)
cm=metrics.confusion_matrix(test_labels,pred)
print(cm)
plt.imshow(cm, cmap='binary')This is how my confusion matrix looks like:
[[3962 325 0 ..., 0 0 0] [ 250 2765 0 ..., 0 0 0] [ 2 8 17 ..., 0 0 0] ..., [ 1 6 0 ..., 5 0 0] [ 1 1 0 ..., 0 0 0] [ 9 0 0 ..., 0 0 9]]However, I do not receive a clear or legible plot. Is there a better way to do this?
03 Answers
you can use plt.matshow() instead of plt.imshow() or you can use seaborn module's heatmap (see documentation) to plot the confusion matrix
import seaborn as sn
import pandas as pd
import matplotlib.pyplot as plt
array = [[33,2,0,0,0,0,0,0,0,1,3], [3,31,0,0,0,0,0,0,0,0,0], [0,4,41,0,0,0,0,0,0,0,1], [0,1,0,30,0,6,0,0,0,0,1], [0,0,0,0,38,10,0,0,0,0,0], [0,0,0,3,1,39,0,0,0,0,4], [0,2,2,0,4,1,31,0,0,0,2], [0,1,0,0,0,0,0,36,0,2,0], [0,0,0,0,0,0,1,5,37,5,1], [3,0,0,0,0,0,0,0,0,39,0], [0,0,0,0,0,0,0,0,0,0,38]]
df_cm = pd.DataFrame(array, index = [i for i in "ABCDEFGHIJK"], columns = [i for i in "ABCDEFGHIJK"])
plt.figure(figsize = (10,7))
sn.heatmap(df_cm, annot=True)@bninopaul 's answer is not completely for beginners
here is the code you can "copy and run"
import seaborn as sn
import pandas as pd
import matplotlib.pyplot as plt
array = [[13,1,1,0,2,0], [3,9,6,0,1,0], [0,0,16,2,0,0], [0,0,0,13,0,0], [0,0,0,0,15,0], [0,0,1,0,0,15]]
df_cm = pd.DataFrame(array, range(6), range(6))
# plt.figure(figsize=(10,7))
sn.set(font_scale=1.4) # for label size
sn.heatmap(df_cm, annot=True, annot_kws={"size": 16}) # font size
plt.show()IF you want more data in you confusion matrix, including "totals column" and "totals line", and percents (%) in each cell, like matlab default (see image below)
including the Heatmap and other options...
You should have fun with the module above, shared in the github ; )
This module can do your task easily and produces the output above with a lot of params to customize your CM:
